2 November 2015

Manawatu sentinel surveillance site

Manawatu campylobacteriosis cases

New Zealand campylobacteriosis cases

MLST distribution of human cases

MLSTs are source specific

MLSTs differ by rurality

MLSTs differ by rurality

Questions

  • Does the proportion of human cases attributed to each source change seasonally?

  • Did the intervention in the poultry industry work?

  • Is attribution related to rurality?

  • Do some genotypes cause human disease more than others?

Modelling

R packages sourceR and islandR

sourceR

Work with Poppy Miller and Chris Jewell, generalising the modified Hald model of Müllner et. al. 2009.

Model human cases \(Y_{itl}\) of genotype \(i\) at time \(t\) in location \(l\) by \[ Y_{itl} \sim \mathsf{Poisson}(q_i \sum_j a_{jtl} p_{ijt}) \]

  • \(a_{jtl}\) captures propensity of source \(j\) to act as a vehicle for disease at time \(t\) in location \(l\).
  • \(p_{ijt}\) is the relative occurrence of type \(i\) on source \(j\) at time \(t\).
  • \(q_i\) captures propensity of genotype \(i\) to be over-represented in human cases.

sourceR genotype effects

islandR

islandR model

\[ P(\mathsf{st}) = \sum_j P(\mathsf{st} \mid \mathsf{source}_j) P(\mathsf{source}_j) \]

islandR model

\[ P(\mathsf{st}) = \sum_j \underbrace{P(\mathsf{st} \mid \mathsf{source}_j)}_\text{genomic model} P(\mathsf{source}_j) \]

islandR model

\[ P(\mathsf{st}) = \sum_j \underbrace{P(\mathsf{st} \mid \mathsf{source}_j)}_\text{genomic model} \underbrace{P(\mathsf{source}_j)}_\text{attribution to source} \]

islandR model

\[ \begin{aligned} &P(\mathsf{st} \mid \underbrace{t, \mathbf{x}}_\text{covariates}) =\\ &\quad \sum_j \underbrace{P(\mathsf{st} \mid \mathsf{source}_j)}_\text{genomic model} \underbrace{P(\mathsf{source}_j \mid t, \mathbf{x})}_\text{attribution with covariates} \end{aligned} \]

islandR attribution

Nested within each source \(j\) we have \[ \begin{aligned} \mathsf{logit}\left(P(\mathsf{source}_j \mid t, \mathbf{x})\right) &= \mathsf{Location}_\mathbf{x} \cdot \mathbf{1}\left[t \geq 2008\right] \cdot \mathsf{Month}_t + \epsilon_{\mathbf{x}t}\\ \epsilon_{\mathbf{x}t} &\sim \mathsf{Normal}(\rho \epsilon_{\mathbf{x}(t-1)}, \sigma^2) \end{aligned} \]

  • Covariates are estimated as a Gibbs step conditional on correlation \(\rho\), variance \(\sigma^2\) and \(P(\mathsf{source}_j \mid t, \mathbf{x})\).

  • \(\phi\) and \(\sigma^2\) are then updated using Gibbs conditional on the covariates and \(P(\mathsf{source}_j \mid t, \mathbf{x})\).

  • \(P(\mathsf{source}_j \mid t, \mathbf{x})\) are then updated from the full conditional prior, interleaved with Metropolis Hastings steps.

Results

Urban attribution

Rural attribution

Urban attribution

Rural attribution

Summary

  • Urban cases tend to be more associated with poultry, and rural cases with ruminants.

  • There does seem to be some evidence for seasonality in attribution.

  • The poultry intervention in 2007 resulted in a marked reduction in poultry related cases in urban areas, less strong in rural areas.

  • Very few cases associated with water or other sources.

  • Limitation: Genomic model assumed constant through time.

  • Two R packages are on the way, sourceR and islandR.

Acknowledgements

  • MidCentral Public Health Services: Tui Shadbolt,
    Adie Transom
  • Medlab Central: Lynn Rogers
  • ESR: Phil Carter
  • mEpiLab: Rukhshana Akhter, Julie Collins-Emerson,
    Ahmed Fayaz, Anne Midwinter, Sarah Moore, Antoine Nohra, Angie Reynolds
  • Ministry for Primary Industries: Donald Campbell,
    Peter van der Logt
  • Poppy Miller, Chris Jewell
  • Petra Müllner
  • Nigel French

Thanks!

Urban water/other attribution

Rural water/other attribution

Urban water/other attribution

Rural water/other attribution